1 University of Copenhagen
2 UCPH

✉ Correspondence: Mikkel Nørtoft <>

Keywords: prestige objects; grave goods; grave wealth; Corded Ware culture; Gini coefficient

Highlights: New open framework for automatic quantification of grave wealth combining a range of different grave good value and wealth aspects such as labour-hours, scarcity, manufacturing skill, raw material distance, prestige/symbolic value.

1 Introduction

Wealth may be defined in different ways in a given society, and may have varying focus in different economies, regions and periods. A general framework for charactarizing main wealth types based on a comparative analysis of ethnographic data was proposed by (Mulder2009?) and (Smith2010?). This includes embodied wealth (e.g. body weight, grip strength, practical skills, and reproductive success), relational wealth (social ties in food- sharing networks and other types of assistance), and material wealth (land, livestock, and house and household goods). Finding the best way to measure inequality has long been a point of interest in modern populations (e.g. United Nations, http://data.un.org/Data.aspx?d=WDI&f=Indicator_Code%3ASI.POV.GINI), and more recently also in prehistoric populations (e.g. Kohler and Smith 2018). The Gini coefficient, which summarizes inequality in a population in a single number between 0 (100% equal) and 1 (100% unequal) based on income, has been a popular tool in modern populations because of the simplicity of the resulting single number and the fact that it can be compared across countries around the world. However, income data are not available for prehistoric populations, and thus, different proxy-measures have been utilized to calculate Gini coefficients. These include grave goods, domestic artefacts, house floor area, and storage sizes (see Smith, Kohler and Feinman 2018 for an overview of studies).

Another measure for modern populations, the Human Development Index (HDI), includes, apart from income, life expectancy, and education in measuring human development in a way that can be compared across countries. Oka et al. (2018), inspired by this measure, created the Composite Archaeological Inequality (CAI) index to combine inequality measures from a range of different material sources (primarily domestic artefacts), in across historical and archaeological sample populations, also with the purpose of comparing populations of different economies.

Calculating wealth in graves is a particularly difficult subject, and so far, Gini coefficients from graves tend to be significantly higher than e.g. house floor areas e.g. (Fochesato, Bogaard and Bowles 2019; Stone 2018), and this study. Often, grave wealth is calculated by assigning value points to each grave good type (e.g. Todorova (2002) for Chalcolithic Durankulak in Bulgaria, and Nieszery et al. (1995: 204-209) for the LBK). However, while both studies give similar, and sensible criteria for assigning values: availability of raw materials (Nieszery, Breinl and Endlicher 1995; Todorova 2002), manufacturing hours (Nieszery, Breinl and Endlicher 1995), scarcity in the graves (Todorova 2002), and more subjective evaluations by the archaeologists working with the materials, they are not transparent about the weighting or quantification of the individual points. Some of these criteria (manufacturing time, import distance, material scarcity) are also mentioned by Olausson (1983: 12-14) for flint axes, who adds morphological exaggeration (extremely long and thin axes, while unusable have higher value), special colour of the material, skill (requiring specialist knowledge/experience). Grossmann (2021) is more transparent about his specific calculations in that he uses exclusively scarcity (total graves/total material frequency in graves, normalised against the richest grave in the population) as a measure of value when making Gini coefficients. Although focusing on only one of these aspects of value, his method does have the appeal that it is simple to apply to a larger array of data. However, as Grossmann also mentions (2021: 30), the method may overestimate simple tools (e.g. bone), if they are deposited rarely in a grave, and underestimate objects considered more valuable (imported raw material, longer chaîne operatoire, more specialised knowledge) such as bronze daggers, if they are deposited more often.

This study seeks to compare and combine different aspects of wealth in graves, including introducing a new, transparent, reproducible, adaptable, and automated value system combining grave good value parameters such as manufacturing time (in person-hours), scarcity (in the definition of Grossmann (2021)), import distance travel-hours), prestige (or symbolic) value based on the median of its Total Object Type range, and manufacturing skill (in 4 levels based on percentage bonuses on manufacturing hours). To ensure that all these value aspects have equal weight despite having very different ranges, they will be normalized, and their mean for each grave will be used in the Gini calculation. Apart from the combined grave good value system, this study will also apply Ginis of grave depth and Total Object Types represented in each grave, as well as house floor area Gini indices of different periods and regions in Central and Northern Europe as a baseline. The three different main measures of grave wealth, and one wealth measure of house size differentiation, will then be combined using the CAI which calculates the geometric mean of different Ginis. Thus, the attempted measure of inequality takes into account as many available aspects as possible, which should give a more robust and balanced measure than a Gini only focused on one aspect (e.g. only labour-hours or scarcity). At the same time, the proposed system is transparent, reproducible, and openly available at github for anyone to apply it to their own data, or even customize or expand it to grave goods represented in other periods or regions.

However, using graves as directly reflecting social inequality through a processual theoretical lense on a population is not straightforward. While there may be correlations between, e.g. metal-bearing or polished stone-bearing graves and more protein or nutrition intake from high trophic food such as meat and dairy reflected in \(\delta^{15}\)N and \(\delta ^{13}\)C stable isotopes (Budd et al. 2020; Masclans Latorre, Bickle and Hamon 2020), this cannot be expected as a universal pattern for such grave goods. Furthermore, \(\delta ^{13}\)C levels may be affected by local environments requiring a thorough baseline, and \(\delta^{15}\)N levels may be affected by manuring of staple crops or increased fish diet, and pastoral communities may have generally more reliance on high trophic food sources such as meat and dairy (Knipper et al. 2020: 128). This could make high trophic diet less diagnostic of lived high status. These caveats should be kept in mind as well when interpreting inequality in the present study.

Even when taking grave goods at face value, it can be difficult to whether they reflect the wealth of the individual in life or wealth transmitted by loved ones (e.g. family/household) at the burial, or in some cases perhaps even by the whole community. Thus material wealth and status in the community may overlap and be closely connected, and it would be difficult to distinguish them completely. However, use-wear studies of grave goods have shown that it may be possible to see if an object has been freshly manufactured (no or little use-wear), perhaps specifically as a grave good, or used or worn for a long time before the burial (Frînculeasa et al. 2020; Masclans Latorre, Bickle and Hamon 2020), either by the individual or those adding their used belongings in the grave. The former could indicate lived material wealth and the latter could indicate transmitted affectionate value. However, the grave goods in this study has not been studied in such detail, and it is therefore not possible to distinguish status and material wealth in this study.

Preservation is another issue in mortuary archaeology, especially organic remains (apart from the skeletal remains) such as textiles, which may well have carried extensive symbolic meaning and would have been very labourious to make, but is almost never preserved. Ritual activities during the burial, which may also have conveyed considerable symbology and perhaps even status, are also very difficult to reconstruct except activities such as the sprinkling of red ochre (although not very common in this case) or feasting which may be indicated by the animal bones deposited in the graves (e.g. quantified by counting species represented in a grave). Secondary manipulation of bones is another matter adding uncertainty to the interpreation of a burial (Kolář 2012).

2 Background

With the rise of ancient genomics, it is now clear that there was a major and relatively abrupt male-biased population influx from the Pontic-Caspian steppe/forest-steppe in 3rd millennium Central and Northern Europe (Papac et al. 2021; Scorrano et al. 2021, and data visualization at https://ancientgenomes.com/), perhaps fueled by a change in herding economy (Wilkin et al. 2021) (gimbutas1993?). Furthermore, this new influx of people correlates extraordinarily well with the linguistic “steppe-hypothesis” of Indo-European language dispersal Anthony and Brown (2017); Anthony (2017); Anthony and Ringe (2015); Chang et al. (2015)], while also borrowing agricultural vocabulary from the Neolithic farmers (e.g. Rune Iversen and Guus Kroonen 2017). Recent archaeogenomic studies have shown that the 3rd millennium Corded Ware and Bell Beaker societies tend to have been patrilocal, patrilinear, and practicing female exogamy (Mittnik et al. 2019; Papac et al. 2021; Sjögren et al. 2020), also supported by reconstructed Indo-European family patterns (Olsen 2019). A non-random decrease in Y-haplogroup diversity from early to middle Corded Ware in Bohemia, and elsewhere during the early 3rd millennium BCE, may reflect competition between male lineages or “an isolated mating network with strictly exclusive social norms” (Papac et al. 2021: 6; Zeng, Aw and Feldman 2018).

The Corded Ware culture has been interpreted as being more mobile with a mixed agriculture and herding and gathering economy (Lechterbeck et al. 2013), and more focused on the individual and the core family than the preceding agricultural societies in Europe (Kristiansen et al. 2017: 343). Corded Ware burial rituals tend to be relatively gender stereotypical reflected in body position (males lying on their right side, females on their left side), and grave goods (males having especially battle-axes, and females having jewellery). While most research has focused on proposed male insignia of power such as “battle-axes”, this study focuses more on how we could attempt to statistically “measure” general inequality in the Corded Ware culture, primarily using the Gini coefficient.

The case used to calculate Gini coefficients was 82 graves of the Moravian Corded Ware culture, which were collected from the catalogues in Šebela and Rakovský (1999) and Kolář (2011). These were selected because they had preserved skeletal remains, which made it easier to connect the grave goods to the buried individual.

3 Materials and methods

Five different Gini coefficients were computed on the grave data:

Secondary manipulations for the Moravian Corded Ware have not been documented very frequently until after the 1970s, and primarily from 1995-2005 (Kolář 2018: 74-78). The majority of the data (65 of 81 graves) are from the catalogue by Sebela which has only excavations from up until 1990 (Šebela and Rakovský 1999: 15), some of which before 1970, and only 12 of these have complete documentation (denoted VN 1 and VN 2 in the supplementary table following the quality system in the catalogue, Šebela and Rakovský (1999): 16). Therefore, undocumented secondary manipulations should be expected for some of the material in this study. Only single graves with preserved skeletal materials were used (except one double grave, Iv4_807A/B, where the grave goods were denoted as connected to each individual, Kolář (2011)), such that grave goods could be connected to the individual with reasonable likelihood. However, it cannot be excluded that some grave goods (or even human remains) could have been removed or added later which was not taken into account in this case study. Objects in the infill occurred in 8 graves (155.1.3 (barrow preserved), 197.1.1, 347.1.5, 97.1.1, Hos_802, Iv4_800, Iv4_807, and Iv4_810, in most cases potsherds and pebbles, but also a few copper items and bone tools). Their role in the burial situation is not clear, and this does add some uncertainty to the data. However, the cases where objects were present in the grave infill are few, and would hardly skew the data or the analyses significantly.

In order to set up a flexible computation system for grave good labour-hours, the data was, based on the data and structure of the tables in Kolář (2011), divided into main materials stone, flint (subdivided into flint axes and other flint), bone tools and ornaments, animal bones, shell ornaments, metal, ceramics (subdivided into pots and other). This was done both for the archaeological data and for the reference data from experimental, ethnographic and prehistoric crafts people sources. The time estimates from the reference data would then form the basis of the time estimates for the archaeological data based on a number of parameters within the chaîne operatoire of each artefact. As an example for pottery vessels (and based on extensive interview with the potter Inger Heebøl at Lejre Land of Legends), size would be a main, and easily quantifiable criterion of time and skill (the largest single measure of the pot gave the best correlation with time, and requires the least from the data archaeological quality). Apart from size, degree of impressed decoration, plastic decoration, polish, smoothing/beating, type and amount of temper, slip or paint and firing (where relevant) were also incorporated as separate factors on the time estimate. However, a minimum time for the smallest and most simple pots was indicated by the reference data # Results
Association of each grave good type with Total Object Types

Figure 3.1: Association of each grave good type with Total Object Types

Figure 3.1 shows the different ranges in Total Object Types for each grave good type assuming a spectrum of 0-10 represented grave good types (TOT). It indicates that some object types are exclusive to the richest graves (gold hair decoration, perforated shells), some are confined to the upper half of the spectrum (perforated tooth beads, stone axes, copper awls/needles and copper knives/razors), and some are more widely distributed from top to bottom, e.g. ceramics. While some perforated shells may have been locally available, the fact that they are only found in 4 of the richest graves in this sample of graves, is mirrored in a larger study of Corded Ware graves (Kyselý, Dobeš and Svoboda 2019).

The medians in this plot, accumulated per grave (not taking into account the count of each type in the graves because this is often unclear), will be used as one of several measures of wealth used in Gini calculations, from here on simply referred to as “prestige”.

The Gini coefficient of inequality was calculated first based on Total Object Types (TOT), on labour-hours, and on a combination of labour-hours, manufacture skill-level, scarcity of the given material, and the minimum time to transport the material from its source (only for materials with minimum 30 km transport), in this case including metals and some stone and flint materials, primarily based on Kolář (2018)).

Table 3.1: Estimated value measures for all Moravian Corded Ware graves.
Grave_ID total_PH_raw prestige_total total_scarcity total_travel total_skill mean median sum
100.3.1. Gr. 1 2.7742118 3.0 1.098404 0.0000000 0.0000000 1.3745232 1.0984043 6.872616
104.1.1.1 Grave 1 (VN 1) 11.2416579 11.0 3.762920 0.0000000 2.3100667 5.6629290 3.7629204 28.314645
121.1.1. Gr. 1 2.6019095 3.0 1.098404 0.0000000 0.5126667 1.4425961 1.0984043 7.212980
132.1.7.2 Barrow 2 (VN 1) 20.4629002 12.0 6.410356 6.8571429 4.4476667 10.0356132 6.8571429 50.178066
14.1.1. Gr. 1 31.1658103 23.5 20.990911 7.8571429 0.6751667 16.8378062 20.9909111 84.189031
143.1.3. Gr. 3 23.5770936 4.0 1.098404 0.0000000 10.6133333 7.8577662 4.0000000 39.288831
143.1.4. Gr. 4 0.0000000 0.0 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 0.000000
143.1.7. Gr. 7 80.4838268 18.5 16.394701 57.1428571 17.1708802 37.9384529 18.5000000 189.692265
143.1.8. Gr. 8 0.0000000 0.0 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 0.000000
148.2.1. Gr. 1 51.1492022 25.5 25.619659 1.4285714 4.5207333 21.6436331 25.5000000 108.218166
155.1.1. Br. 3 33.2587374 18.5 8.738824 0.0000000 11.1911667 14.3377456 11.1911667 71.688728
155.1.2 Barrow 4 (VN 1) 58.9314421 23.5 8.721744 1.4285714 18.8288333 22.2821181 18.8288333 111.410591
155.1.3. Br. 5 35.9623318 12.5 20.990911 0.7142857 3.6905000 14.7716057 12.5000000 73.858029
155.1.4 Barrow 6 (VN 1) 234.4252677 48.5 164.349223 186.4285714 51.9825372 137.1371198 164.3492229 685.685599
173.1.4. Gr. 4 25.1471084 11.5 8.746552 28.5714286 6.3994177 16.0729014 11.5000000 80.364507
177.1.1 Grave 1 (VN 1) 1762.8222061 39.0 63.516622 85.7142857 229.1557221 436.0417673 85.7142857 2180.208836
177.4.5. Gr. 5 24.2750521 11.0 3.762920 0.0000000 8.7070667 9.5490078 8.7070667 47.745039
197.1.1 65.5669565 21.0 16.386972 28.5714286 13.0371790 28.9125072 21.0000000 144.562536
198.1.1. Gr. 1 31.5506556 11.5 8.746552 28.5714286 7.4892545 17.5715782 11.5000000 87.857891
199.1.2. Gr. 2 34.1180751 7.0 1.098404 0.0000000 12.3503333 10.9133625 7.0000000 54.566813
20.1.1. Gr. 1 54.5604146 16.5 65.125969 14.2857143 1.7550667 30.4454330 16.5000000 152.227165
207.1.1. Gr. 1 11.5449890 3.0 1.098404 0.0000000 1.1853333 3.3657453 1.1853333 16.828727
207.1.3. Gr. 11 64.3315437 11.5 29.912358 0.0000000 10.6613333 23.2810469 11.5000000 116.405235
207.1.6. Gr. 18 12.5588980 7.0 3.762920 0.0000000 3.1643333 5.2972303 3.7629204 26.486152
232.1.1 Grave 5 (VN 1) 87.6607961 7.0 41.978853 115.7142857 18.5846023 54.1877073 41.9788525 270.938537
232.1.2. Gr. 14 68.5080958 31.5 70.101873 14.2857143 7.2900667 38.3371500 31.5000000 191.685750
24.2.1. Gr. 1 3.3981526 3.0 1.098404 0.0000000 0.4126667 1.5818447 1.0984043 7.909224
240.1.1. Br. 1 19.1859322 13.0 8.721744 0.7142857 6.5466667 9.6337257 8.7217438 48.168628
246.1.1. Gr. 1 9.8557974 3.0 1.098404 0.0000000 1.7306667 3.1369737 1.7306667 15.684868
246.3.1. Gr. 1 17.5401397 7.0 1.098404 0.0000000 3.4595000 5.8196088 3.4595000 29.098044
25.4.2 Grave 1 2.6545748 4.0 1.098404 0.0000000 0.0000000 1.5505958 1.0984043 7.752979
257.1.1. Gr. 1 7.9608394 4.0 1.098404 0.0000000 2.5816667 3.1281821 2.5816667 15.640910
271.1.1. Gr. 1: Skel. 1 24.2675328 15.0 6.410356 0.7142857 6.6453333 10.6075016 6.6453333 53.037508
273.1.1. Gr. 1 20.7689388 11.0 6.410356 0.7142857 5.0003333 8.7787828 6.4103563 43.893914
277.1.1. Gr. 1 75.7128340 15.5 51.786176 0.0000000 7.0799000 30.0157820 15.5000000 150.078910
281.1.1. Gr. 1 22.8649102 16.5 16.386972 28.5714286 3.2447852 17.5136192 16.5000000 87.568096
291.1.1. Gr. 1 17.1708683 7.0 3.745840 0.7142857 3.9033333 6.5068655 3.9033333 32.534328
294.1.1 Grave 5 (VN 1) 4.2344948 3.0 1.098404 0.0000000 0.4826667 1.7631131 1.0984043 8.815566
30.1.1 Grave 9 20.0000000 4.5 19.209302 0.0000000 0.0000000 8.7418605 4.5000000 43.709302
30.1.2 Grave 36 (VN 1) 51.2532971 26.0 11.386260 29.2857143 14.2742000 26.4398943 26.0000000 132.199471
30.1.3 Grave 42 VN 1 11.5820734 7.5 10.703055 0.0000000 0.0000000 5.9570258 7.5000000 29.785129
30.1.4 Grave 70 (VN I) 12.5998042 7.5 10.703055 0.0000000 0.0000000 6.1605719 7.5000000 30.802860
312.1.1 Grave 1 (VN 1) 46.1551353 22.0 31.666188 22.1428571 8.2740000 26.0476361 22.1428571 130.238181
314.1.2. Gr. 2 25.5476677 7.5 8.746552 28.5714286 3.5465518 14.7824401 8.7465524 73.912200
321.3.1. Gr. 3 12.3994909 9.0 6.074308 0.0000000 3.5816667 6.2110931 6.0743079 31.055465
321.3.2. Gr. 4 0.0000000 0.0 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 0.000000
347.1.1 Grave 1 16.7551829 11.0 3.762920 0.0000000 4.7938333 7.2623873 4.7938333 36.311937
347.1.3. Gr. 3 5.6660619 12.0 6.410356 0.7142857 0.0025000 4.9586408 5.6660619 24.793204
347.1.5 Grave 5 (VN 3b) 11.1839698 9.0 25.141253 14.2857143 0.5566667 12.0335207 11.1839698 60.167604
35.1.1 Grave 1 (VN 3a) 5.0156197 3.0 1.098404 0.0000000 0.7653333 1.9758715 1.0984043 9.879357
35.1.2 Grave 2 (VN 3a) 19.6726847 7.0 1.098404 0.0000000 7.4710000 7.0484178 7.0000000 35.242089
355.1.2. Gr. 2 62.0364729 26.5 30.595562 1.4285714 10.0177333 26.1156680 26.5000000 130.578340
368.1.1 Grave 1 59.7055675 32.5 40.164707 57.8571429 8.6883005 39.7831435 40.1647067 198.915718
56.1.1. Gr. 1 16.5737228 8.0 3.745840 0.7142857 6.1341667 7.0336031 6.1341667 35.168015
58.1.1. Gr. 1 31.1312916 26.0 25.991623 28.5714286 3.4098143 23.0208316 26.0000000 115.104158
72.1.1. Gr. 1 3.8646063 5.0 6.074308 0.0000000 0.0000000 2.9877828 3.8646063 14.938914
72.1.2 Grave 2 (VN 2) 16.9405359 8.0 6.410356 0.7142857 4.1920667 7.2514489 6.4103563 36.257245
93.1.1. Gr.2 0.8456837 3.0 1.098404 0.0000000 0.0000000 0.9888176 0.8456837 4.944088
93.1.2. Gr. 26 24.2596784 12.5 15.678959 0.0000000 2.1130000 10.9103275 12.5000000 54.551637
93.1.3. Gr. 36 0.8506365 0.0 1.098404 0.0000000 0.0000000 0.3898081 0.0000000 1.949041
93.2.1. Gr. 1 20.9831385 8.0 3.745840 0.7142857 6.5370000 7.9960529 6.5370000 39.980264
93.2.2. Gr. 2 9.8928651 7.0 1.098404 0.0000000 1.0153333 3.8013205 1.0984043 19.006603
93.2.3. Gr. 3 0.0000000 0.0 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 0.000000
93.2.4. Gr. 4 0.0000000 0.0 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 0.000000
97.1.1 Grave 1 (VN 1) 96.5992903 34.0 26.682556 66.4285714 16.7697210 48.0960278 34.0000000 240.480139
Hos_801 37.6118431 16.0 18.351204 28.5714286 6.2308648 21.3530680 18.3512036 106.765340
Hos_802 59.0606313 23.5 25.619659 6.8571429 7.7480667 24.5570999 23.5000000 122.785500
Hos_838 126.9208654 21.0 25.999352 57.1428571 20.0310985 50.2188346 25.9993517 251.094173
Hos_839 45.8399824 11.5 29.912358 0.0000000 4.5643333 18.3633347 11.5000000 91.816673
Iv3_2_801 18.0241251 16.0 8.738824 0.0000000 4.6979000 9.4921698 8.7388240 47.460849
Iv3_2_803 13.4222124 7.0 3.762920 0.0000000 4.1104000 5.6591066 4.1104000 28.295533
Iv3_2_804 0.9217826 3.0 1.098404 0.0000000 0.0000000 1.0040374 0.9217826 5.020187
Iv3_2_805 15.1942106 4.5 8.746552 28.5714286 2.7742365 11.9572856 8.7465524 59.786428
Iv3_2_806 3.1442319 7.0 3.762920 0.0000000 0.0050000 2.7824305 3.1442319 13.912152
Iv3_2_809 16.1865517 15.0 13.722456 28.5714286 2.8867627 15.2734398 15.0000000 76.367199
Iv3_2_811 6.6480330 3.0 1.098404 0.0000000 1.0403333 2.3573541 1.0984043 11.786771
Iv3_2_825 23.1667981 15.0 18.004300 28.5714286 7.5952333 18.4675519 18.0042997 92.337760
Iv4_800 50.7031693 21.5 16.386972 28.5714286 11.5753560 25.7473852 21.5000000 128.736926
Iv4_803 0.0000000 0.0 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 0.000000
Iv4_807A 1.9471973 3.0 1.098404 0.0000000 0.3876667 1.2866537 1.0984043 6.433268
Iv4_807B 1.0000000 1.0 2.647436 1.4285714 0.0000000 1.2152015 1.0000000 6.076007
Iv4_810 140.6482499 42.5 60.472413 97.8571429 19.4847175 72.1925047 60.4724133 360.962524

The Gini coefficients for the 82 Moravian Corded Ware graves in this dataset are
Table 3.2: Gini coefficients based on TOT, raw person-hours, and scarcity.
gini lwr.ci upr.ci
Gini_TOT 0.410 0.378 0.453
Gini_graveDepth 0.393 0.366 0.429
Gini_GG_median 0.615 0.560 0.689
Gini_GG_normed 0.551 0.503 0.611
Table 3.3: Summaries of the data foundation of the Gini coefficients.
Min. 1st Qu. Median Mean 3rd Qu. Max.
Total Object Types 0 1.00 3.00 2.96 4.00 10.00
person-hours 0 6.98 19.43 51.17 43.78 1762.82
scarcity 0 1.10 6.41 15.02 18.99 164.35
grave depth 2 27.50 55.00 61.44 78.25 250.00
prestige 0 4.00 9.00 12.07 16.38 48.50
Grave good median 0 1.94 7.00 13.52 17.63 164.35
Grave good normalized mean 0 0.02 0.06 0.10 0.13 0.73
CWC house sizes 31 65.00 90.00 85.07 105.00 131.92

The Gini coefficients are based on the data summarized in table 3.3

Lorenz curves for grave depth, Total Object Types, manufacture person-hours, and scarcity (sensu Grossmann 2021).

Figure 3.2: Lorenz curves for grave depth, Total Object Types, manufacture person-hours, and scarcity (sensu Grossmann 2021).

The Lorenz curves for the 3 different inequality measures (TOT, combined grave good value, and grave depth) are shown in figure 3.2

If we include all the mentioned aspects of grave good value, i.e. person-hours, scarcity, import travel hours (at 7 km/h), and prestige, and combine them into one wealth measure as the median of them all.

For comparison, Gini indices from house area data over different periods and regions of Central and Nothern Europe were calculated as well using house measurement data from Schunke and Stäuble (2019) (German and Czech Unetice), (balfanz_etal15?) (German, Swiss, French, and Dutch Bell Beaker), (schmalfuss18?) (German Bell Beaker), (schmalfuss_etal18?) (MBA-LBA), García Diaz (2017) (Dutch Corded Ware), Sparrevohn, Kastholm and Nielsen (2019) and Sørensen (2015) (Scandinavian Neolithic). The house area Gini indices and mean areas over time are given in figure 3.3.

Figure 3.3: Gini indices and mean size of Neolithic to Bronze Age houses

The geometric mean (i.e. the CAI) of these combined Gini coefficient and the Ginis for Total Object Types (TOT), and grave pit depth is then 0.463, or when the combined measure is the mean of the normalized grave good measures, we get a CAI of 0.446. Fochesato et al. (2019: 13-15, and table S5), based on four cases with both graves and houses (Late Neolithic Gomolava in the Western Balkans, Early Dynastic Kafajah, Old Babylonian Ur and Neo Babylonian Ur in southern Mesopotamia), find that the difference between grave good and house Ginis is 0.244-0.343 (or an average of 28.1%) for which they downgrade the grave Gini to match the house Gini. However, all four cases are agricultural (some even state) societies, and there may be different variations depending on the social norms and economy of a given society. Furthermore, making house sizes the gold standard of the expected Gini level, may neglect houses that were not built with wooden posts (tents, small huts, etc.), and thus less visible to archaeologists, or clear enough to allow measurements of size (the basis for house size Ginis). A similar argument can be made for missing parts of the population in grave data which may well have been at the lower tier of society or even slaves. Fochesato et al. (2019: table S4) reconstruct the missing population in Southern Mesopotamia to be 34% and in Roman (rural?) Italy to 9%. Both of these are state societies, and presumably much more based on systematic slave labour than what we might expect for Neolithic societies. If we accept the increasingly overwhelming correlation between the migrations from the steppe in the 3rd millennium BCE, and the spread of Indo-European languages, we may also get a hint of how institutionalized slavery was in steppe-derived populations such as the Corded Ware by looking at reconstructed “Core-Indo-European” (Proto-Indo-European excluding the Anatolian branch) vocabulary of slavery. While such vocabulary is widespread in the daughter languages, and while military, conquering activities, and “bounty/booty” are easier to reconstruct, it seems difficult to reconstruct a word clearly unambiguously meaning “slave” or “servant” (Nørtoft (2017): 84-89); (campanile98?): 16-17). However, females may have been taken by force together with cattle and other goods, and incorporated into the family while conquered males, judging from textual evidence, were more likely killed instead ((campanile98?): 16-17). This scenario can be supported also by ancient genomics which shows that former Neolithic maternal lineages were often incorporated into the European steppe-derived gene pool, while paternal lineages of the Neolithic farmers were largely replaced by steppe-derived paternal lineages (Olalde et al. (2017); Olalde et al. (2019); Sjögren et al. (2020); Papac et al. (2021)). However, Mittnik et al. (2019) have also shown that non-local females could sometimes take high status roles in Bell Beaker and Bronze Age society, thus making the role of female “captives” (marriage alliance or voluntary movement is of course also possible) more varied than just being subdued to slave labour. Such integration into society could support the argument that we should not expect a large quantity of missing “unfree” in these cultures. Thus, if we expect a missing population of “unfree” for the Corded Ware data to be (probably much) less than 9%, the impact of missing people on the Gini index, at least extrapolating from the estimates by Fochesato et al., would be negligable. In this case, even for the more conservative normalized grave CAI (and not accounting for missing populations), the difference between house and grave Gini is 52.2%. If we instead accept that the “true Gini” approximation lies somewhere between the Gini for house sizes and the CAI from graves (using here the CAI with normalized grave good values), we may again apply the CAI to these two values which would give a combined house and grave CAI of 0.261. This, instead of a fixed 28.1% grave > house Gini difference, gives a more flexible combined measure which allows for more extreme grave wealth expenditures to be acknowledged by pulling harder on the house Gini, while still downplaying the general grave wealth exaggeration to meet house Ginis. If we only had a Gini for houses, or only a CAI for graves, we could then (albeit still speculatively until more studies have been done) attempt to extrapolate from this example that the grave CAI is overestimated from the combined house-grave CAI by 41.5, and house Ginis are underestimated by 18.4. And thus correct accordingly for comparison between case studies.

4 Discussion

The relatively small adjustments to the Gini or CAI may seem insignificant, but the combined measure should be more robust because it includes more of the aspects that define value in ethnographic parallels (based on flint axes, Olausson (1983)). It would be preferrable to combine the gini(s) or CAI from grave data with Ginis of house size or storage size in the same period or culture. However, settlements are lacking for Moravian Corded Ware making this impossible. Nevertheless, this study

5 Conclusion

6 Acknowledgements

7 References

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